We show that silicon-based metagratings capable of large-angle, multifunctional performance can be realized using inverse freeform design. These devices consist of nonintuitive nanoscale patterns and support a large number of spatially overlapping optical modes per unit area. The quantity of modes, in combination with their optimized responses, provides the degrees of freedom required to produce high-efficiency devices. To demonstrate the power and versatility of our approach, we fabricate metagratings that can efficiently deflect light to 75° angles and multifunctional devices that can steer beams to different diffraction orders based on wavelength. A theoretical analysis of the Bloch modes supported by these devices elucidates the spatial mode profiles and coupling dynamics that make high-performance beam deflection possible. This approach represents a new paradigm in nano-optical mode engineering and utilizes different physics from the current state-of-the-art, which is based on the stitching of noninteracting waveguide structures. We envision that inverse design will enable new classes of high-performance photonic systems and new strategies toward the nanoscale control of light fields.

A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices. Design methods based on iterative optimization can push the performance limits of metasurfaces, but they require extensive computational resources that limit their implementation to small numbers of microscale devices. We show that generative neural networks can train from images of periodic, topology-optimized metagratings to produce high-efficiency, topologically complex devices operating over a broad range of deflection angles and wavelengths. Further iterative optimization of these designs yields devices with enhanced robustness and efficiencies, and these devices can be utilized as additional training data for network refinement. In this manner, generative networks can be trained, with a onetime computation cost, and used as a design tool to facilitate the production of near-optimal, topologically-complex device designs. We envision that such data-driven design methodologies can apply to other physical sciences domains that require the design of functional elements operating across a wide parameter space.

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Metasurfaces are ultrathin optical elements that are highly promising for constructing lightweight and compact optical systems. For their practical implementation, it is imperative to maximize the metasurface efficiency. Topology optimization provides a pathway for pushing the limits of metasurface efficiency; however, topology optimization methods have been limited to the design of microscale devices due to the extensive computational resources that are required. We introduce a new strategy for optimizing large-area metasurfaces in a computationally efficient manner. By stitching together individually optimized sections of the metasurface, we can reduce the computational complexity of the optimization from high-polynomial to linear. As a proof of concept, we design and experimentally demonstrate large-area, high-numerical-aperture silicon metasurface lenses with focusing efficiencies exceeding 90%. These concepts can be generalized to the design of multifunctional, broadband diffractive optical devices and will enable the implementation of large-area, high-performance metasurfaces in practical optical systems.

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